Package evaluation to test Tsunami on Julia 1.10.10 (df86fe4d49*) started at 2026-02-04T05:47:13.280 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.10` Set-up completed after 3.14s ################################################################################ # Installation # Installing Tsunami... Resolving package versions... Installed GPUArraysCore ─────────────── v0.2.0 Installed Crayons ───────────────────── v4.1.1 Installed ColorTypes ────────────────── v0.12.1 Installed RealDot ───────────────────── v0.1.0 Installed IrrationalConstants ───────── v0.2.6 Installed ImageCore ─────────────────── v0.10.5 Installed IRTools ───────────────────── v0.4.15 Installed Transducers ───────────────── v0.4.85 Installed MLCore ────────────────────── v1.0.0 Installed JLD2 ──────────────────────── v0.6.3 Installed DiffRules ─────────────────── v1.15.1 Installed Adapt ─────────────────────── v4.4.0 Installed OffsetArrays ──────────────── v1.17.0 Installed ArgCheck ──────────────────── v2.5.0 Installed Flux ──────────────────────── v0.16.9 Installed SciMLPublic ───────────────── v1.0.1 Installed Zstd_jll ──────────────────── v1.5.7+1 Installed TensorCore ────────────────── v0.1.1 Installed Functors ──────────────────── v0.5.2 Installed Preferences ───────────────── v1.5.1 Installed ContextVariablesX ─────────── v0.1.3 Installed ShowCases ─────────────────── v0.1.0 Installed PtrArrays ─────────────────── v1.3.0 Installed Accessors ─────────────────── v0.1.43 Installed TableTraits ───────────────── v1.0.1 Installed DiffResults ───────────────── v1.1.0 Installed ADTypes ───────────────────── v1.21.0 Installed ProgressLogging ───────────── v0.1.6 Installed SpecialFunctions ──────────── v2.6.1 Installed FLoopsBase ────────────────── v0.1.1 Installed HashArrayMappedTries ──────── v0.2.0 Installed Tables ────────────────────── v1.12.1 Installed DataAPI ───────────────────── v1.16.0 Installed ChunkCodecLibZlib ─────────── v1.0.0 Installed NNlib ─────────────────────── v0.9.33 Installed Setfield ──────────────────── v1.1.2 Installed FixedPointNumbers ─────────── v0.8.5 Installed StaticArrays ──────────────── v1.9.16 Installed JLLWrappers ───────────────── v1.7.1 Installed Baselet ───────────────────── v0.1.1 Installed Zygote ────────────────────── v0.7.10 Installed ChunkCodecLibZstd ─────────── v1.0.0 Installed StaticArraysCore ──────────── v1.4.4 Installed AbstractFFTs ──────────────── v1.5.0 Installed OneHotArrays ──────────────── v0.2.10 Installed NaNMath ───────────────────── v1.1.3 Installed IteratorInterfaceExtensions ─ v1.0.0 Installed ConstructionBase ──────────── v1.6.0 Installed PrecompileTools ───────────── v1.2.1 Installed Optimisers ────────────────── v0.4.7 Installed ColorVectorSpace ──────────── v0.11.0 Installed DataValueInterfaces ───────── v1.0.0 Installed MicroCollections ──────────── v0.2.0 Installed DefineSingletons ──────────── v0.1.2 Installed OrderedCollections ────────── v1.8.1 Installed EnumX ─────────────────────── v1.0.6 Installed EnzymeCore ────────────────── v0.8.18 Installed LLVMExtra_jll ─────────────── v0.0.38+0 Installed StructArrays ──────────────── v0.7.2 Installed NameResolution ────────────── v0.1.5 Installed ChunkCodecCore ────────────── v1.0.1 Installed ScopedValues ──────────────── v1.5.0 Installed ChainRulesCore ────────────── v1.26.0 Installed FileIO ────────────────────── v1.18.0 Installed MLDataDevices ─────────────── v1.17.4 Installed CEnum ─────────────────────── v0.5.0 Installed DelimitedFiles ────────────── v1.9.1 Installed InitialValues ─────────────── v0.3.1 Installed GPUArrays ─────────────────── v11.4.0 Installed ProtoBuf ──────────────────── v1.2.0 Installed TensorBoardLogger ─────────── v0.1.26 Installed Reexport ──────────────────── v1.2.2 Installed FillArrays ────────────────── v1.16.0 Installed ForwardDiff ───────────────── v1.3.2 Installed LogExpFunctions ───────────── v0.3.29 Installed BFloat16s ─────────────────── v0.6.1 Installed BSON ──────────────────────── v0.3.9 Installed MappedArrays ──────────────── v0.4.3 Installed Colors ────────────────────── v0.13.1 Installed DataStructures ────────────── v0.19.3 Installed CommonSubexpressions ──────── v0.3.1 Installed Requires ──────────────────── v1.3.1 Installed BufferedStreams ───────────── v1.2.2 Installed ChainRules ────────────────── v1.72.6 Installed SplittablesBase ───────────── v0.1.15 Installed AliasTables ───────────────── v1.1.3 Installed KernelAbstractions ────────── v0.9.39 Installed PaddedViews ───────────────── v0.5.12 Installed StackViews ────────────────── v0.1.2 Installed ZygoteRules ───────────────── v0.2.7 Installed MacroTools ────────────────── v0.5.16 Installed BangBang ──────────────────── v0.4.7 Installed PrettyPrint ───────────────── v0.2.0 Installed MLStyle ───────────────────── v0.4.17 Installed StatsAPI ──────────────────── v1.8.0 Installed OpenSpecFun_jll ───────────── v0.5.6+0 Installed Compat ────────────────────── v4.18.1 Installed CompositionsBase ──────────── v0.1.2 Installed UnPack ────────────────────── v1.0.2 Installed UnsafeAtomics ─────────────── v0.3.0 Installed MLUtils ───────────────────── v0.4.8 Installed Tsunami ───────────────────── v0.3.1 Installed InverseFunctions ──────────── v0.1.17 Installed JuliaVariables ────────────── v0.2.4 Installed SimpleTraits ──────────────── v0.9.5 Installed StatsBase ─────────────────── v0.34.10 Installed SparseInverseSubset ───────── v0.1.2 Installed SortingAlgorithms ─────────── v1.2.2 Installed FLoops ────────────────────── v0.2.2 Installed LLVM ──────────────────────── v9.4.6 Installed Missings ──────────────────── v1.2.0 Installed Atomix ────────────────────── v1.1.2 Installed DocStringExtensions ───────── v0.9.5 Installed MosaicViews ───────────────── v0.3.4 Updating `~/.julia/environments/v1.10/Project.toml` [36e41bbe] + Tsunami v0.3.1 Updating `~/.julia/environments/v1.10/Manifest.toml` [47edcb42] + ADTypes v1.21.0 [621f4979] + AbstractFFTs v1.5.0 [7d9f7c33] + Accessors v0.1.43 [79e6a3ab] + Adapt v4.4.0 [66dad0bd] + AliasTables v1.1.3 [dce04be8] + ArgCheck v2.5.0 [a9b6321e] + Atomix v1.1.2 [ab4f0b2a] + BFloat16s v0.6.1 [fbb218c0] + BSON v0.3.9 [198e06fe] + BangBang v0.4.7 [9718e550] + Baselet v0.1.1 [e1450e63] + BufferedStreams v1.2.2 [fa961155] + CEnum v0.5.0 [082447d4] + ChainRules v1.72.6 [d360d2e6] + ChainRulesCore v1.26.0 [0b6fb165] + ChunkCodecCore v1.0.1 [4c0bbee4] + ChunkCodecLibZlib v1.0.0 [55437552] + ChunkCodecLibZstd v1.0.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.18.1 [a33af91c] + CompositionsBase v0.1.2 [187b0558] + ConstructionBase v1.6.0 [6add18c4] + ContextVariablesX v0.1.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [864edb3b] + DataStructures v0.19.3 [e2d170a0] + DataValueInterfaces v1.0.0 [244e2a9f] + DefineSingletons v0.1.2 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [ffbed154] + DocStringExtensions v0.9.5 [4e289a0a] + EnumX v1.0.6 [f151be2c] + EnzymeCore v0.8.18 [cc61a311] + FLoops v0.2.2 [b9860ae5] + FLoopsBase v0.1.1 [5789e2e9] + FileIO v1.18.0 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 [587475ba] + Flux v0.16.9 [f6369f11] + ForwardDiff v1.3.2 [d9f16b24] + Functors v0.5.2 [0c68f7d7] + GPUArrays v11.4.0 [46192b85] + GPUArraysCore v0.2.0 [076d061b] + HashArrayMappedTries v0.2.0 [7869d1d1] + IRTools v0.4.15 [a09fc81d] + ImageCore v0.10.5 [22cec73e] + InitialValues v0.3.1 [3587e190] + InverseFunctions v0.1.17 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [033835bb] + JLD2 v0.6.3 [692b3bcd] + JLLWrappers v1.7.1 [b14d175d] + JuliaVariables v0.2.4 [63c18a36] + KernelAbstractions v0.9.39 [929cbde3] + LLVM v9.4.6 [2ab3a3ac] + LogExpFunctions v0.3.29 [c2834f40] + MLCore v1.0.0 [7e8f7934] + MLDataDevices v1.17.4 [d8e11817] + MLStyle v0.4.17 [f1d291b0] + MLUtils v0.4.8 [1914dd2f] + MacroTools v0.5.16 [dbb5928d] + MappedArrays v0.4.3 [128add7d] + MicroCollections v0.2.0 [e1d29d7a] + Missings v1.2.0 [e94cdb99] + MosaicViews v0.3.4 [872c559c] + NNlib v0.9.33 [77ba4419] + NaNMath v1.1.3 [71a1bf82] + NameResolution v0.1.5 [6fe1bfb0] + OffsetArrays v1.17.0 [0b1bfda6] + OneHotArrays v0.2.10 [3bd65402] + Optimisers v0.4.7 [bac558e1] + OrderedCollections v1.8.1 [5432bcbf] + PaddedViews v0.5.12 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.5.1 [8162dcfd] + PrettyPrint v0.2.0 [33c8b6b6] + ProgressLogging v0.1.6 [3349acd9] + ProtoBuf v1.2.0 [43287f4e] + PtrArrays v1.3.0 [c1ae055f] + RealDot v0.1.0 [189a3867] + Reexport v1.2.2 [ae029012] + Requires v1.3.1 [431bcebd] + SciMLPublic v1.0.1 [7e506255] + ScopedValues v1.5.0 [efcf1570] + Setfield v1.1.2 [605ecd9f] + ShowCases v0.1.0 [699a6c99] + SimpleTraits v0.9.5 [a2af1166] + SortingAlgorithms v1.2.2 [dc90abb0] + SparseInverseSubset v0.1.2 [276daf66] + SpecialFunctions v2.6.1 [171d559e] + SplittablesBase v0.1.15 [cae243ae] + StackViews v0.1.2 [90137ffa] + StaticArrays v1.9.16 [1e83bf80] + StaticArraysCore v1.4.4 [82ae8749] + StatsAPI v1.8.0 [2913bbd2] + StatsBase v0.34.10 [09ab397b] + StructArrays v0.7.2 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [899adc3e] + TensorBoardLogger v0.1.26 [62fd8b95] + TensorCore v0.1.1 [28d57a85] + Transducers v0.4.85 [36e41bbe] + Tsunami v0.3.1 [3a884ed6] + UnPack v1.0.2 [013be700] + UnsafeAtomics v0.3.0 [e88e6eb3] + Zygote v0.7.10 [700de1a5] + ZygoteRules v0.2.7 [dad2f222] + LLVMExtra_jll v0.0.38+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [3161d3a3] + Zstd_jll v1.5.7+1 [0dad84c5] + ArgTools v1.1.1 [56f22d72] + Artifacts [2a0f44e3] + Base64 [8bf52ea8] + CRC32c [ade2ca70] + Dates [8ba89e20] + Distributed [f43a241f] + Downloads v1.6.0 [7b1f6079] + FileWatching [9fa8497b] + Future [b77e0a4c] + InteractiveUtils [4af54fe1] + LazyArtifacts [b27032c2] + LibCURL v0.6.4 [76f85450] + LibGit2 [8f399da3] + Libdl [37e2e46d] + LinearAlgebra [56ddb016] + Logging [d6f4376e] + Markdown [a63ad114] + Mmap [ca575930] + NetworkOptions v1.2.0 [44cfe95a] + Pkg v1.10.0 [de0858da] + Printf [3fa0cd96] + REPL [9a3f8284] + Random [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization [6462fe0b] + Sockets [2f01184e] + SparseArrays v1.10.0 [10745b16] + Statistics v1.10.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test [cf7118a7] + UUIDs [4ec0a83e] + Unicode [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] + LibCURL_jll v8.4.0+0 [e37daf67] + LibGit2_jll v1.6.4+0 [29816b5a] + LibSSH2_jll v1.11.0+1 [c8ffd9c3] + MbedTLS_jll v2.28.1010+0 [14a3606d] + MozillaCACerts_jll v2025.12.2 [4536629a] + OpenBLAS_jll v0.3.23+5 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.2.1+1 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 [8e850ede] + nghttp2_jll v1.52.0+1 [3f19e933] + p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 9.56s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling packages... 660.1 ms ✓ TestEnv 1 dependency successfully precompiled in 2 seconds ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ TestEnv ~/.julia/packages/TestEnv/h9a3r/src/julia-1.9/activate_set.jl:63 Precompiling package dependencies... Precompiling packages... 844.1 ms ✓ SentinelArrays 693.3 ms ✓ StructTypes 461.6 ms ✓ AbstractFFTs 346.9 ms ✓ LaTeXStrings 357.9 ms ✓ ExprTools 382.2 ms ✓ Glob 303.9 ms ✓ IteratorInterfaceExtensions 1763.0 ms ✓ UnsafeAtomics 366.7 ms ✓ TensorCore 387.9 ms ✓ WorkerUtilities 343.4 ms ✓ StatsAPI 538.1 ms ✓ ADTypes 399.6 ms ✓ ChunkCodecCore 559.0 ms ✓ InitialValues 346.6 ms ✓ CEnum 554.6 ms ✓ ConcurrentUtilities 744.9 ms ✓ OffsetArrays 314.6 ms ✓ UnPack 891.9 ms ✓ FillArrays 386.1 ms ✓ InverseFunctions 424.9 ms ✓ BufferedStreams 358.3 ms ✓ PrettyPrint 362.2 ms ✓ ArgCheck 411.4 ms ✓ ShowCases 339.2 ms ✓ CompilerSupportLibraries_jll 1623.1 ms ✓ MacroTools 452.2 ms ✓ Preferences 766.4 ms ✓ MbedTLS 479.6 ms ✓ Compat 522.7 ms ✓ OrderedCollections 332.8 ms ✓ InternedStrings 398.7 ms ✓ Requires 304.2 ms ✓ DataValueInterfaces 342.5 ms ✓ EnumX 362.6 ms ✓ StructIO 309.0 ms ✓ RealDot 316.7 ms ✓ Reexport 471.2 ms ✓ DocStringExtensions 567.5 ms ✓ InlineStrings 896.5 ms ✓ IrrationalConstants 492.5 ms ✓ URIs 326.7 ms ✓ SimpleBufferStream 459.0 ms ✓ BFloat16s 465.4 ms ✓ Infinities 325.2 ms ✓ CompositionsBase 302.3 ms ✓ TestItems 437.5 ms ✓ ExceptionUnwrapping 322.4 ms ✓ PtrArrays 502.5 ms ✓ TranscodingStreams 371.1 ms ✓ HashArrayMappedTries 325.4 ms ✓ DefineSingletons 354.8 ms ✓ LazyModules 409.9 ms ✓ NaNMath 648.0 ms ✓ BSON 398.1 ms ✓ DelimitedFiles 355.1 ms ✓ InvertedIndices 10804.2 ms ✓ MLStyle 583.0 ms ✓ EnzymeCore 400.2 ms ✓ ConstructionBase 358.3 ms ✓ DataAPI 312.4 ms ✓ PackageExtensionCompat 782.1 ms ✓ Crayons 403.7 ms ✓ ProgressLogging 351.1 ms ✓ BitFlags 15232.7 ms ✓ Unitful 346.9 ms ✓ Scratch 438.8 ms ✓ LoggingExtras 316.2 ms ✓ SciMLPublic 397.5 ms ✓ StaticArraysCore 402.7 ms ✓ MappedArrays 843.7 ms ✓ Baselet 495.6 ms ✓ GZip 496.6 ms ✓ ZipFile 588.2 ms ✓ SuiteSparse 766.9 ms ✓ Statistics 1215.1 ms ✓ AbstractFFTs → AbstractFFTsTestExt 306.9 ms ✓ TableTraits 421.4 ms ✓ Atomix 420.1 ms ✓ ChunkCodecLibZlib 380.5 ms ✓ StackViews 406.8 ms ✓ PaddedViews 719.6 ms ✓ FillArrays → FillArraysSparseArraysExt 351.2 ms ✓ InverseFunctions → InverseFunctionsDatesExt 348.6 ms ✓ NameResolution 574.6 ms ✓ CommonSubexpressions 1727.9 ms ✓ IRTools 985.8 ms ✓ SimpleTraits 408.7 ms ✓ JLLWrappers 515.9 ms ✓ MPIPreferences 354.1 ms ✓ PrecompileTools 339.4 ms ✓ Compat → CompatLinearAlgebraExt 378.5 ms ✓ Adapt 1837.7 ms ✓ FileIO 2602.6 ms ✓ ProtoBuf 1817.1 ms ✓ ObjectFile 591.9 ms ✓ LogExpFunctions 7668.8 ms ✓ TestItemRunner 402.0 ms ✓ AliasTables 409.8 ms ✓ CodecZlib 367.2 ms ✓ ScopedValues 346.3 ms ✓ ADTypes → ADTypesEnzymeCoreExt 340.3 ms ✓ ConstructionBase → ConstructionBaseLinearAlgebraExt 438.5 ms ✓ PooledArrays 399.6 ms ✓ Missings 400.8 ms ✓ StridedViews 946.2 ms ✓ Unitful → PrintfExt 362.3 ms ✓ DiffResults 606.2 ms ✓ SparseInverseSubset 1807.4 ms ✓ FixedPointNumbers 763.1 ms ✓ Tables 419.3 ms ✓ MosaicViews 697.8 ms ✓ FillArrays → FillArraysStatisticsExt 552.3 ms ✓ InverseFunctions → InverseFunctionsTestExt 357.5 ms ✓ CompositionsBase → CompositionsBaseInverseFunctionsExt 4213.0 ms ✓ JuliaVariables 476.8 ms ✓ OpenSSL_jll 469.0 ms ✓ Xorg_libpciaccess_jll 468.9 ms ✓ Chemfiles_jll 477.3 ms ✓ libaec_jll 467.0 ms ✓ LibTracyClient_jll 522.8 ms ✓ Enzyme_jll 579.4 ms ✓ LLVMExtra_jll 480.5 ms ✓ Zstd_jll 382.5 ms ✓ MicrosoftMPI_jll 467.8 ms ✓ Libiconv_jll 480.2 ms ✓ OpenSpecFun_jll 406.1 ms ✓ MPItrampoline_jll 5818.9 ms ✓ StaticArrays 1232.8 ms ✓ StringManipulation 7055.2 ms ✓ Parsers 721.0 ms ✓ FilePathsBase 1024.7 ms ✓ ChainRulesCore 366.7 ms ✓ ContextVariablesX 1550.2 ms ✓ DataStructures 611.4 ms ✓ Adapt → AdaptSparseArraysExt 405.9 ms ✓ GPUArraysCore 377.6 ms ✓ OffsetArrays → OffsetArraysAdaptExt 343.2 ms ✓ EnzymeCore → AdaptExt 601.7 ms ✓ NPZ 390.5 ms ✓ LogExpFunctions → LogExpFunctionsInverseFunctionsExt 368.7 ms ✓ ADTypes → ADTypesConstructionBaseExt 544.7 ms ✓ Functors 1107.5 ms ✓ Setfield 344.4 ms ✓ StridedViews → StridedViewsPtrArraysExt 963.4 ms ✓ Unitful → ConstructionBaseUnitfulExt 938.1 ms ✓ Unitful → NaNMathExt 2477.2 ms ✓ PeriodicTable 3441.4 ms ✓ UnitfulAtomic 977.2 ms ✓ Unitful → InverseFunctionsUnitfulExt 1502.4 ms ✓ ColorTypes 725.0 ms ✓ StructArrays 948.6 ms ✓ MLCore 1864.7 ms ✓ Accessors 1456.5 ms ✓ OpenSSL 1193.0 ms ✓ Tracy 4595.0 ms ✓ LLVM 440.2 ms ✓ ChunkCodecLibZstd 430.2 ms ✓ StringEncodings 488.4 ms ✓ XML2_jll 2207.1 ms ✓ SpecialFunctions 973.1 ms ✓ StaticArrays → StaticArraysStatisticsExt 739.6 ms ✓ FillArrays → FillArraysStaticArraysExt 681.0 ms ✓ ConstructionBase → ConstructionBaseStaticArraysExt 631.9 ms ✓ Adapt → AdaptStaticArraysExt 14125.3 ms ✓ PrettyTables 4724.4 ms ✓ JSON3 411.5 ms ✓ InlineStrings → ParsersExt 331.8 ms ✓ FilePathsBase → FilePathsBaseMmapExt 631.9 ms ✓ ChainRulesCore → ChainRulesCoreSparseArraysExt 973.4 ms ✓ ZygoteRules 394.1 ms ✓ AbstractFFTs → AbstractFFTsChainRulesCoreExt 375.4 ms ✓ ADTypes → ADTypesChainRulesCoreExt 495.5 ms ✓ EnzymeCore → EnzymeCoreChainRulesCoreExt 1206.9 ms ✓ LogExpFunctions → LogExpFunctionsChainRulesCoreExt 688.2 ms ✓ StaticArrays → StaticArraysChainRulesCoreExt 369.6 ms ✓ FLoopsBase 454.5 ms ✓ SortingAlgorithms 691.5 ms ✓ MLDataDevices 1035.0 ms ✓ SplittablesBase 2526.4 ms ✓ AtomsBase 1942.3 ms ✓ ColorVectorSpace 3512.6 ms ✓ Colors 448.0 ms ✓ StructArrays → StructArraysLinearAlgebraExt 761.1 ms ✓ Accessors → LinearAlgebraExt 9703.8 ms ✓ HTTP 456.6 ms ✓ LLVM → BFloat16sExt 10355.9 ms ✓ GPUCompiler 1057.4 ms ✓ UnsafeAtomics → UnsafeAtomicsLLVM 16847.1 ms ✓ JLD2 1789.4 ms ✓ Pickle 478.8 ms ✓ Hwloc_jll 545.9 ms ✓ DiffRules 12738.3 ms ✓ ArrayLayouts 2972.8 ms ✓ KernelAbstractions 686.0 ms ✓ WeakRefStrings 877.9 ms ✓ FilePathsBase → FilePathsBaseTestExt 1092.5 ms ✓ Optimisers 1476.5 ms ✓ SpecialFunctions → SpecialFunctionsChainRulesCoreExt 1992.1 ms ✓ StatsBase 27830.9 ms ✓ DataFrames 560.0 ms ✓ MLDataDevices → ChainRulesCoreExt 427.0 ms ✓ MLDataDevices → FillArraysExt 645.4 ms ✓ MLDataDevices → SparseArraysExt 2735.3 ms ✓ Chemfiles 843.2 ms ✓ ColorVectorSpace → SpecialFunctionsExt 13664.2 ms ✓ ImageCore 4170.5 ms ✓ ColorSchemes 379.0 ms ✓ StructArrays → StructArraysAdaptExt 660.0 ms ✓ StructArrays → StructArraysSparseArraysExt 735.9 ms ✓ StructArrays → StructArraysStaticArraysExt 477.1 ms ✓ Accessors → StructArraysExt 592.0 ms ✓ Accessors → TestExt 1010.3 ms ✓ Accessors → UnitfulExt 773.1 ms ✓ Accessors → StaticArraysExt 727.3 ms ✓ BangBang 1736.3 ms ✓ DataDeps 969.0 ms ✓ FileIO → HTTPExt 32209.4 ms ✓ Enzyme 715.3 ms ✓ JLD2 → UnPackExt 390.4 ms ✓ OpenMPI_jll 506.0 ms ✓ MPICH_jll 2860.0 ms ✓ ForwardDiff 1531.8 ms ✓ ArrayLayouts → ArrayLayoutsSparseArraysExt 698.2 ms ✓ KernelAbstractions → LinearAlgebraExt 9347.9 ms ✓ CSV 423.3 ms ✓ Optimisers → OptimisersEnzymeCoreExt 399.1 ms ✓ Optimisers → OptimisersAdaptExt 2194.4 ms ✓ ImageBase 4438.7 ms ✓ TensorBoardLogger 5023.2 ms ✓ ChainRules 778.2 ms ✓ BangBang → BangBangStaticArraysExt 521.1 ms ✓ BangBang → BangBangChainRulesCoreExt 476.8 ms ✓ BangBang → BangBangTablesExt 859.3 ms ✓ MicroCollections 20687.1 ms ✓ Enzyme → EnzymeStaticArraysExt 15532.0 ms ✓ Enzyme → EnzymeChainRulesCoreExt 15472.4 ms ✓ Enzyme → EnzymeLogExpFunctionsExt 15462.4 ms ✓ Enzyme → EnzymeBFloat16sExt 15428.4 ms ✓ Enzyme → EnzymeGPUArraysCoreExt 688.0 ms ✓ HDF5_jll 784.6 ms ✓ ForwardDiff → ForwardDiffStaticArraysExt 3201.5 ms ✓ LazyArrays 1011.6 ms ✓ KernelAbstractions → SparseArraysExt 787.8 ms ✓ KernelAbstractions → EnzymeExt 776.6 ms ✓ StructArrays → StructArraysGPUArraysCoreExt 1765.5 ms ✓ ImageShow 22375.3 ms ✓ Zygote 849.4 ms ✓ MLDataDevices → ChainRulesExt 2195.2 ms ✓ BangBang → BangBangDataFramesExt 500.4 ms ✓ BangBang → BangBangStructArraysExt 2514.4 ms ✓ Transducers 16070.4 ms ✓ Enzyme → EnzymeSpecialFunctionsExt 4504.9 ms ✓ HDF5 1629.8 ms ✓ LazyArrays → LazyArraysStaticArraysExt 4190.9 ms ✓ GPUArrays 5453.5 ms ✓ NNlib 1949.1 ms ✓ Zygote → ZygoteColorsExt 1644.3 ms ✓ MLDataDevices → ZygoteExt 2222.8 ms ✓ Transducers → TransducersDataFramesExt 670.1 ms ✓ Transducers → TransducersAdaptExt 5133.8 ms ✓ FLoops 1836.6 ms ✓ MAT 2155.9 ms ✓ InfiniteArrays 1981.0 ms ✓ Transducers → TransducersLazyArraysExt 1749.8 ms ✓ GPUArrays → JLD2Ext 1422.4 ms ✓ MLDataDevices → GPUArraysSparseArraysExt 1301.9 ms ✓ NNlib → NNlibEnzymeCoreExt 1202.4 ms ✓ NNlib → NNlibSpecialFunctionsExt 1380.3 ms ✓ OneHotArrays 6153.9 ms ✓ MLUtils 1732.3 ms ✓ InfiniteArrays → InfiniteArraysStatisticsExt 1263.1 ms ✓ NNlib → NNlibForwardDiffExt 1196.0 ms ✓ MLDataDevices → OneHotArraysExt 1872.9 ms ✓ MLDataDevices → MLUtilsExt 8107.9 ms ✓ MLDatasets 1937.2 ms ✓ ParameterSchedulers 7096.2 ms ✓ Flux 23333.5 ms ✓ Flux → FluxEnzymeExt 6148.1 ms ✓ Tsunami 25624.3 ms ✓ Tsunami → TsunamiEnzymeExt 280 dependencies successfully precompiled in 639 seconds. 7 already precompiled. Precompilation completed after 664.99s ################################################################################ # Testing # Testing Tsunami ┌ Warning: Could not use exact versions of packages in manifest, re-resolving └ @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.10/Pkg/src/Operations.jl:1829 Status `/tmp/jl_dxCJ5B/Project.toml` [a93c6f00] DataFrames v1.8.1 [7da242da] Enzyme v0.13.129 [587475ba] Flux v0.16.9 [d9f16b24] Functors v0.5.2 [7e8f7934] MLDataDevices v1.17.4 [eb30cadb] MLDatasets v0.7.20 [f1d291b0] MLUtils v0.4.8 [3bd65402] Optimisers v0.4.7 [d7d3b36b] ParameterSchedulers v0.4.3 [189a3867] Reexport v1.2.2 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 [36e41bbe] Tsunami v0.3.1 [44cfe95a] Pkg v1.10.0 [9a3f8284] Random [10745b16] Statistics v1.10.0 [8dfed614] Test Status `/tmp/jl_dxCJ5B/Manifest.toml` [47edcb42] ADTypes v1.21.0 [621f4979] AbstractFFTs v1.5.0 [7d9f7c33] Accessors v0.1.43 [79e6a3ab] Adapt v4.4.0 [66dad0bd] AliasTables v1.1.3 [dce04be8] ArgCheck v2.5.0 [4c555306] ArrayLayouts v1.12.2 [a9b6321e] Atomix v1.1.2 [a963bdd2] AtomsBase v0.5.2 ⌅ [ab4f0b2a] BFloat16s v0.5.1 [fbb218c0] BSON v0.3.9 [198e06fe] BangBang v0.4.7 [9718e550] Baselet v0.1.1 [d1d4a3ce] BitFlags v0.1.9 [e1450e63] BufferedStreams v1.2.2 [fa961155] CEnum v0.5.0 [336ed68f] CSV v0.10.15 [082447d4] ChainRules v1.72.6 [d360d2e6] ChainRulesCore v1.26.0 [46823bd8] Chemfiles v0.10.43 [0b6fb165] ChunkCodecCore v1.0.1 [4c0bbee4] ChunkCodecLibZlib v1.0.0 [55437552] ChunkCodecLibZstd v1.0.0 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [c3611d14] ColorVectorSpace v0.11.0 [5ae59095] Colors v0.13.1 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.18.1 [a33af91c] CompositionsBase v0.1.2 [f0e56b4a] ConcurrentUtilities v2.5.0 [187b0558] ConstructionBase v1.6.0 [6add18c4] ContextVariablesX v0.1.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [124859b0] DataDeps v0.7.13 [a93c6f00] DataFrames v1.8.1 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [244e2a9f] DefineSingletons v0.1.2 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [ffbed154] DocStringExtensions v0.9.5 [4e289a0a] EnumX v1.0.6 [7da242da] Enzyme v0.13.129 [f151be2c] EnzymeCore v0.8.18 [460bff9d] ExceptionUnwrapping v0.1.11 [e2ba6199] ExprTools v0.1.10 [cc61a311] FLoops v0.2.2 [b9860ae5] FLoopsBase v0.1.1 [5789e2e9] FileIO v1.18.0 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [53c48c17] FixedPointNumbers v0.8.5 [587475ba] Flux v0.16.9 [f6369f11] ForwardDiff v1.3.2 [d9f16b24] Functors v0.5.2 [0c68f7d7] GPUArrays v11.4.0 [46192b85] GPUArraysCore v0.2.0 [61eb1bfa] GPUCompiler v1.8.2 [92fee26a] GZip v0.6.2 [c27321d9] Glob v1.4.0 [f67ccb44] HDF5 v0.17.2 [cd3eb016] HTTP v1.10.19 [076d061b] HashArrayMappedTries v0.2.0 [7869d1d1] IRTools v0.4.15 [c817782e] ImageBase v0.1.7 [a09fc81d] ImageCore v0.10.5 [4e3cecfd] ImageShow v0.3.8 ⌅ [4858937d] InfiniteArrays v0.14.4 [e1ba4f0e] Infinities v0.1.12 [22cec73e] InitialValues v0.3.1 [842dd82b] InlineStrings v1.4.5 [7d512f48] InternedStrings v0.7.0 [3587e190] InverseFunctions v0.1.17 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [033835bb] JLD2 v0.6.3 [692b3bcd] JLLWrappers v1.7.1 [0f8b85d8] JSON3 v1.14.3 [b14d175d] JuliaVariables v0.2.4 [63c18a36] KernelAbstractions v0.9.39 [929cbde3] LLVM v9.4.6 [b964fa9f] LaTeXStrings v1.4.0 [5078a376] LazyArrays v2.9.5 [8cdb02fc] LazyModules v0.3.1 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [23992714] MAT v0.11.4 [c2834f40] MLCore v1.0.0 [7e8f7934] MLDataDevices v1.17.4 [eb30cadb] MLDatasets v0.7.20 [d8e11817] MLStyle v0.4.17 [f1d291b0] MLUtils v0.4.8 [3da0fdf6] MPIPreferences v0.1.11 [1914dd2f] MacroTools v0.5.16 [dbb5928d] MappedArrays v0.4.3 [739be429] MbedTLS v1.1.9 [128add7d] MicroCollections v0.2.0 [e1d29d7a] Missings v1.2.0 [e94cdb99] MosaicViews v0.3.4 [872c559c] NNlib v0.9.33 [15e1cf62] NPZ v0.4.3 [77ba4419] NaNMath v1.1.3 [71a1bf82] NameResolution v0.1.5 [d8793406] ObjectFile v0.5.0 [6fe1bfb0] OffsetArrays v1.17.0 [0b1bfda6] OneHotArrays v0.2.10 [4d8831e6] OpenSSL v1.6.1 [3bd65402] Optimisers v0.4.7 [bac558e1] OrderedCollections v1.8.1 [65ce6f38] PackageExtensionCompat v1.0.2 [5432bcbf] PaddedViews v0.5.12 [d7d3b36b] ParameterSchedulers v0.4.3 [69de0a69] Parsers v2.8.3 [7b2266bf] PeriodicTable v1.2.1 [fbb45041] Pickle v0.3.6 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.5.1 [8162dcfd] PrettyPrint v0.2.0 [08abe8d2] PrettyTables v3.1.2 [33c8b6b6] ProgressLogging v0.1.6 [3349acd9] ProtoBuf v1.2.0 [43287f4e] PtrArrays v1.3.0 [c1ae055f] RealDot v0.1.0 [189a3867] Reexport v1.2.2 [ae029012] Requires v1.3.1 [431bcebd] SciMLPublic v1.0.1 [7e506255] ScopedValues v1.5.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.9 [efcf1570] Setfield v1.1.2 [605ecd9f] ShowCases v0.1.0 [777ac1f9] SimpleBufferStream v1.2.0 [699a6c99] SimpleTraits v0.9.5 [a2af1166] SortingAlgorithms v1.2.2 [dc90abb0] SparseInverseSubset v0.1.2 [276daf66] SpecialFunctions v2.6.1 [171d559e] SplittablesBase v0.1.15 [cae243ae] StackViews v0.1.2 [90137ffa] StaticArrays v1.9.16 [1e83bf80] StaticArraysCore v1.4.4 [82ae8749] StatsAPI v1.8.0 [2913bbd2] StatsBase v0.34.10 [4db3bf67] StridedViews v0.4.3 [69024149] StringEncodings v0.3.7 [892a3eda] StringManipulation v0.4.2 [09ab397b] StructArrays v0.7.2 [53d494c1] StructIO v0.3.1 [856f2bd8] StructTypes v1.11.0 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [899adc3e] TensorBoardLogger v0.1.26 [62fd8b95] TensorCore v0.1.1 [f8b46487] TestItemRunner v1.1.4 [1c621080] TestItems v1.0.0 [e689c965] Tracy v0.1.6 [3bb67fe8] TranscodingStreams v0.11.3 [28d57a85] Transducers v0.4.85 [36e41bbe] Tsunami v0.3.1 [5c2747f8] URIs v1.6.1 [3a884ed6] UnPack v1.0.2 [1986cc42] Unitful v1.28.0 [a7773ee8] UnitfulAtomic v1.0.0 [013be700] UnsafeAtomics v0.3.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [a5390f91] ZipFile v0.10.1 [e88e6eb3] Zygote v0.7.10 [700de1a5] ZygoteRules v0.2.7 [78a364fa] Chemfiles_jll v0.10.4+0 [7cc45869] Enzyme_jll v0.0.249+0 ⌅ [0234f1f7] HDF5_jll v1.14.6+0 [e33a78d0] Hwloc_jll v2.12.2+0 [dad2f222] LLVMExtra_jll v0.0.38+0 [ad6e5548] LibTracyClient_jll v0.13.1+0 [94ce4f54] Libiconv_jll v1.18.0+0 [7cb0a576] MPICH_jll v4.3.2+0 [f1f71cc9] MPItrampoline_jll v5.5.4+0 [9237b28f] MicrosoftMPI_jll v10.1.4+3 [fe0851c0] OpenMPI_jll v5.0.9+0 [458c3c95] OpenSSL_jll v3.5.5+0 [efe28fd5] OpenSpecFun_jll v0.5.6+0 ⌅ [02c8fc9c] XML2_jll v2.13.9+0 [a65dc6b1] Xorg_libpciaccess_jll v0.18.1+0 [3161d3a3] Zstd_jll v1.5.7+1 [477f73a3] libaec_jll v1.1.5+0 [0dad84c5] ArgTools v1.1.1 [56f22d72] Artifacts [2a0f44e3] Base64 [8bf52ea8] CRC32c [ade2ca70] Dates [8ba89e20] Distributed [f43a241f] Downloads v1.6.0 [7b1f6079] FileWatching [9fa8497b] Future [b77e0a4c] InteractiveUtils [4af54fe1] LazyArtifacts [b27032c2] LibCURL v0.6.4 [76f85450] LibGit2 [8f399da3] Libdl [37e2e46d] LinearAlgebra [56ddb016] Logging [d6f4376e] Markdown [a63ad114] Mmap [ca575930] NetworkOptions v1.2.0 [44cfe95a] Pkg v1.10.0 [de0858da] Printf [3fa0cd96] REPL [9a3f8284] Random [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization [6462fe0b] Sockets [2f01184e] SparseArrays v1.10.0 [10745b16] Statistics v1.10.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [a4e569a6] Tar v1.10.0 [8dfed614] Test [cf7118a7] UUIDs [4ec0a83e] Unicode [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [deac9b47] LibCURL_jll v8.4.0+0 [e37daf67] LibGit2_jll v1.6.4+0 [29816b5a] LibSSH2_jll v1.11.0+1 [c8ffd9c3] MbedTLS_jll v2.28.1010+0 [14a3606d] MozillaCACerts_jll v2025.12.2 [4536629a] OpenBLAS_jll v0.3.23+5 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.2.1+1 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.52.0+1 [3f19e933] p7zip_jll v17.4.0+2 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... ┌ Warning: No functional GPU backend found! Defaulting to CPU. │ │ 1. If no GPU is available, nothing needs to be done. Set `MLDATADEVICES_SILENCE_WARN_NO_GPU=1` to silence this warning. │ 2. If GPU is available, load the corresponding trigger package. │ a. `CUDA.jl` and `cuDNN.jl` (or just `LuxCUDA.jl`) for NVIDIA CUDA Support. │ b. `AMDGPU.jl` for AMD GPU ROCM Support. │ c. `Metal.jl` for Apple Metal GPU Support. (Experimental) │ d. `oneAPI.jl` for Intel oneAPI GPU Support. (Experimental) │ e. `OpenCL.jl` for OpenCL support. (Experimental) └ @ MLDataDevices.Internal ~/.julia/packages/MLDataDevices/4qHOT/src/internal.jl:114 [ Info: GPUs available: false, used: false [ Info: Model Summary: LinearModel() # 1_000 parameters, plus 1_000 non-trainable [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters BatchNorm(3), # 6 parameters, plus 6 Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 6 trainable arrays, 29 parameters, # plus 2 non-trainable, 6 parameters, summarysize 652 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.35 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.27 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 100%|██████████████████████| Time: 0:00:00 ( 0.25 ms/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.13 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Testing: 100%|████████████████████████████| Time: 0:00:00 (18.38 ms/it) a: 1.0 b: 2.0 Validation: 100%|█████████████████████████| Time: 0:00:00 (26.25 ms/it) a: 1.0 b: 2.0 [ Info: GPUs available: false, used: false [ Info: Model Summary: TestModule1( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), 1, 2, ) # Total: 4 arrays, 23 parameters, 372 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_1 Train Epoch 1: 50%|███████████ | ETA: 0:00:10 (10.34 s/it) Train Epoch 1: 100%|██████████████████████| Time: 0:00:10 ( 5.17 s/it) Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.33 ms/it) [ Info: GPUs available: false, used: false [ Info: Model Summary: TBLoggingModule( Chain( Dense(4 => 3, relu), # 15 parameters Dense(3 => 2), # 8 parameters ), true, true, true, true, ) # Total: 4 arrays, 23 parameters, 364 bytes. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_2 Train Epoch 1: 50%|███████████ | ETA: 0:00:01 ( 1.41 s/it) train/batch_idx_step: 1 train/loss_step: 4.37     Train Epoch 1: 100%|██████████████████████| Time: 0:00:01 ( 0.71 s/it) train/batch_idx_step: 2 train/loss_step: 1.52   Train Epoch 2: 100%|██████████████████████| Time: 0:00:00 ( 0.32 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 3: 100%|██████████████████████| Time: 0:00:00 ( 0.20 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51   Train Epoch 4: 100%|██████████████████████| Time: 0:00:00 ( 0.18 ms/it) train/batch_idx_step: 2 train/loss_step: 1.51 Test Summary: | Pass Total Time Package | 67 67 3m29.5s [ Info: GPUs available: false, used: false [ Info: Model Summary: LinearModel() # 1_000 parameters, plus 1_000 non-trainable [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( Dense(784 => 128, relu), # 100_480 parameters Dense(128 => 10), # 1_290 parameters ), :classification, ) # Total: 4 arrays, 101_770 parameters, 397.828 KiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:956 │ [11] materialize! │ @ ./broadcast.jl:914 │ [12] materialize! │ @ ./broadcast.jl:911 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] preprocess_args │ @ ./broadcast.jl:987 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:986 │ [11] preprocess │ @ ./broadcast.jl:983 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:956 │ [14] materialize! │ @ ./broadcast.jl:914 │ [15] materialize! │ @ ./broadcast.jl:911 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( Dense(784 => 128, relu), # 100_480 parameters Dense(128 => 10), # 1_290 parameters ), :classification, ) # Total: 4 arrays, 101_770 parameters, 397.828 KiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. WARNING: Wrapping `Vararg` directly in UnionAll is deprecated (wrap the tuple instead). You may need to write `f(x::Vararg{T})` rather than `f(x::Vararg{<:T})` or `f(x::Vararg{T}) where T` instead of `f(x::Vararg{T} where T)`. ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [10] copyto! │ @ ./broadcast.jl:956 │ [11] materialize! │ @ ./broadcast.jl:914 │ [12] materialize! │ @ ./broadcast.jl:911 │ [13] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:114 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 ┌ Warning: TODO forward zero-set of arraycopy of found (false) eltype (nothing) used memset rather than runtime type │ Caused by: │ Stacktrace: │ [1] copy │ @ ./array.jl:411 │ [2] unaliascopy │ @ ./abstractarray.jl:1496 │ [3] unalias │ @ ./abstractarray.jl:1480 │ [4] broadcast_unalias │ @ ./broadcast.jl:977 │ [5] preprocess │ @ ./broadcast.jl:984 │ [6] preprocess_args │ @ ./broadcast.jl:987 │ [7] preprocess_args │ @ ./broadcast.jl:986 │ [8] preprocess │ @ ./broadcast.jl:983 │ [9] preprocess_args │ @ ./broadcast.jl:987 │ [10] preprocess_args (repeats 2 times) │ @ ./broadcast.jl:986 │ [11] preprocess │ @ ./broadcast.jl:983 │ [12] override_bc_copyto! │ @ ~/.julia/packages/Enzyme/S3nC6/src/compiler/interpreter.jl:818 │ [13] copyto! │ @ ./broadcast.jl:956 │ [14] materialize! │ @ ./broadcast.jl:914 │ [15] materialize! │ @ ./broadcast.jl:911 │ [16] #logsoftmax!#208 │ @ ~/.julia/packages/NNlib/srXYX/src/softmax.jl:117 └ @ Enzyme.Compiler ~/.julia/packages/Enzyme/S3nC6/src/rules/llvmrules.jl:514 Test Summary: | Pass Total Time Package | 4 4 2m14.4s [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/test/tsunami_logs/run_3 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/examples/MLP_MNIST/tsunami_logs/run_1 Val Epoch 0: 4%|█ | ETA: 0:00:02 (65.53 ms/it) accuracy/val: 0.133 loss/val: 2.34     Val Epoch 0: 100%|████████████████████████| Time: 0:00:00 ( 7.00 ms/it) accuracy/val: 0.108 loss/val: 2.35   Train Epoch 1: 0%| | ETA: 1:08:44 ( 9.80 s/it) accuracy/train: 0.125 loss/train: 2.36     Train Epoch 1: 1%|▎ | ETA: 0:13:45 ( 1.98 s/it) accuracy/train: 0.555 loss/train: 2.46     Train Epoch 1: 3%|▋ | ETA: 0:05:41 ( 0.83 s/it) accuracy/train: 0.812 loss/train: 0.589     Train Epoch 1: 5%|█ | ETA: 0:03:34 ( 0.53 s/it) accuracy/train: 0.859 loss/train: 0.382     Train Epoch 1: 6%|█▍ | ETA: 0:02:35 ( 0.39 s/it) accuracy/train: 0.898 loss/train: 0.368     Train Epoch 1: 8%|█▊ | ETA: 0:02:01 ( 0.31 s/it) accuracy/train: 0.93 loss/train: 0.214     Train Epoch 1: 9%|██▏ | ETA: 0:01:39 ( 0.26 s/it) accuracy/train: 0.914 loss/train: 0.386     Train Epoch 1: 11%|██▌ | ETA: 0:01:24 ( 0.22 s/it) accuracy/train: 0.883 loss/train: 0.359     Train Epoch 1: 12%|██▊ | ETA: 0:01:16 ( 0.21 s/it) accuracy/train: 0.953 loss/train: 0.223     Train Epoch 1: 14%|███ | ETA: 0:01:07 ( 0.19 s/it) accuracy/train: 0.938 loss/train: 0.233     Train Epoch 1: 15%|███▍ | ETA: 0:01:00 ( 0.17 s/it) accuracy/train: 0.922 loss/train: 0.282     Train Epoch 1: 17%|███▊ | ETA: 0:00:53 ( 0.15 s/it) accuracy/train: 0.898 loss/train: 0.355     Train Epoch 1: 19%|████▏ | ETA: 0:00:47 ( 0.14 s/it) accuracy/train: 0.844 loss/train: 0.471     Train Epoch 1: 21%|████▋ | ETA: 0:00:42 ( 0.13 s/it) accuracy/train: 0.922 loss/train: 0.244     Train Epoch 1: 23%|█████ | ETA: 0:00:38 ( 0.12 s/it) accuracy/train: 0.914 loss/train: 0.251     Train Epoch 1: 25%|█████▍ | ETA: 0:00:35 ( 0.11 s/it) accuracy/train: 0.891 loss/train: 0.337     Train Epoch 1: 27%|█████▉ | ETA: 0:00:32 ( 0.10 s/it) accuracy/train: 0.953 loss/train: 0.175     Train Epoch 1: 28%|██████▎ | ETA: 0:00:29 (97.54 ms/it) accuracy/train: 0.938 loss/train: 0.184     Train Epoch 1: 30%|██████▋ | ETA: 0:00:27 (92.99 ms/it) accuracy/train: 0.961 loss/train: 0.159     Train Epoch 1: 32%|███████ | ETA: 0:00:25 (88.32 ms/it) accuracy/train: 0.953 loss/train: 0.126     Train Epoch 1: 34%|███████▌ | ETA: 0:00:23 (84.18 ms/it) accuracy/train: 0.953 loss/train: 0.167     Train Epoch 1: 36%|███████▉ | ETA: 0:00:21 (80.47 ms/it) accuracy/train: 0.961 loss/train: 0.224     Train Epoch 1: 38%|████████▎ | ETA: 0:00:20 (77.14 ms/it) accuracy/train: 0.961 loss/train: 0.105     Train Epoch 1: 40%|████████▊ | ETA: 0:00:18 (74.13 ms/it) accuracy/train: 0.938 loss/train: 0.155     Train Epoch 1: 41%|█████████▏ | ETA: 0:00:17 (71.72 ms/it) accuracy/train: 0.953 loss/train: 0.16     Train Epoch 1: 43%|█████████▍ | ETA: 0:00:16 (69.51 ms/it) accuracy/train: 0.969 loss/train: 0.114     Train Epoch 1: 45%|█████████▉ | ETA: 0:00:15 (67.17 ms/it) accuracy/train: 0.938 loss/train: 0.188     Train Epoch 1: 47%|██████████▎ | ETA: 0:00:14 (65.02 ms/it) accuracy/train: 0.969 loss/train: 0.142     Train Epoch 1: 48%|██████████▋ | ETA: 0:00:13 (63.29 ms/it) accuracy/train: 0.945 loss/train: 0.186     Train Epoch 1: 50%|███████████ | ETA: 0:00:12 (61.44 ms/it) accuracy/train: 0.945 loss/train: 0.142     Train Epoch 1: 52%|███████████▌ | ETA: 0:00:12 (59.72 ms/it) accuracy/train: 0.961 loss/train: 0.151     Train Epoch 1: 54%|███████████▉ | ETA: 0:00:11 (58.13 ms/it) accuracy/train: 0.953 loss/train: 0.122     Train Epoch 1: 56%|████████████▎ | ETA: 0:00:10 (56.64 ms/it) accuracy/train: 0.938 loss/train: 0.275     Train Epoch 1: 58%|████████████▋ | ETA: 0:00:09 (55.42 ms/it) accuracy/train: 0.938 loss/train: 0.204     Train Epoch 1: 59%|█████████████▏ | ETA: 0:00:09 (54.11 ms/it) accuracy/train: 0.961 loss/train: 0.156     Train Epoch 1: 61%|█████████████▌ | ETA: 0:00:08 (52.88 ms/it) accuracy/train: 0.969 loss/train: 0.0727     Train Epoch 1: 63%|█████████████▉ | ETA: 0:00:08 (51.72 ms/it) accuracy/train: 0.977 loss/train: 0.134     Train Epoch 1: 65%|██████████████▍ | ETA: 0:00:07 (50.63 ms/it) accuracy/train: 0.953 loss/train: 0.204     Train Epoch 1: 67%|██████████████▊ | ETA: 0:00:06 (49.74 ms/it) accuracy/train: 0.953 loss/train: 0.1     Train Epoch 1: 68%|███████████████▏ | ETA: 0:00:06 (48.89 ms/it) accuracy/train: 0.922 loss/train: 0.179     Train Epoch 1: 70%|███████████████▍ | ETA: 0:00:06 (48.08 ms/it) accuracy/train: 0.945 loss/train: 0.136     Train Epoch 1: 72%|███████████████▊ | ETA: 0:00:05 (47.31 ms/it) accuracy/train: 0.984 loss/train: 0.0673     Train Epoch 1: 74%|████████████████▎ | ETA: 0:00:05 (46.46 ms/it) accuracy/train: 0.953 loss/train: 0.156     Train Epoch 1: 76%|████████████████▋ | ETA: 0:00:04 (45.65 ms/it) accuracy/train: 0.961 loss/train: 0.106     Train Epoch 1: 77%|█████████████████ | ETA: 0:00:04 (44.88 ms/it) accuracy/train: 0.992 loss/train: 0.0921     Train Epoch 1: 79%|█████████████████▌ | ETA: 0:00:03 (44.15 ms/it) accuracy/train: 0.961 loss/train: 0.102     Train Epoch 1: 81%|█████████████████▉ | ETA: 0:00:03 (43.45 ms/it) accuracy/train: 0.938 loss/train: 0.246     Train Epoch 1: 83%|██████████████████▎ | ETA: 0:00:03 (42.79 ms/it) accuracy/train: 0.961 loss/train: 0.222     Train Epoch 1: 85%|██████████████████▋ | ETA: 0:00:02 (42.23 ms/it) accuracy/train: 0.922 loss/train: 0.249     Train Epoch 1: 87%|███████████████████▏ | ETA: 0:00:02 (41.62 ms/it) accuracy/train: 0.945 loss/train: 0.0799     Train Epoch 1: 89%|███████████████████▌ | ETA: 0:00:01 (41.04 ms/it) accuracy/train: 0.969 loss/train: 0.0823     Train Epoch 1: 91%|███████████████████▉ | ETA: 0:00:01 (40.48 ms/it) accuracy/train: 0.953 loss/train: 0.177     Train Epoch 1: 92%|████████████████████▎ | ETA: 0:00:01 (40.01 ms/it) accuracy/train: 0.93 loss/train: 0.184     Train Epoch 1: 94%|████████████████████▊ | ETA: 0:00:00 (39.49 ms/it) accuracy/train: 0.969 loss/train: 0.0834     Train Epoch 1: 96%|█████████████████████▏| ETA: 0:00:00 (38.99 ms/it) accuracy/train: 0.977 loss/train: 0.0641     Train Epoch 1: 98%|█████████████████████▌| ETA: 0:00:00 (38.51 ms/it) accuracy/train: 0.961 loss/train: 0.0656     Train Epoch 1: 100%|█████████████████████▉| ETA: 0:00:00 (38.11 ms/it) accuracy/train: 0.961 loss/train: 0.13     Train Epoch 1: 100%|██████████████████████| Time: 0:00:16 (38.00 ms/it) accuracy/train: 0.955 loss/train: 0.142   Val Epoch 1: 74%|█████████████████▉ | ETA: 0:00:00 ( 2.88 ms/it) accuracy/val: 0.966 loss/val: 0.106        Val Epoch 1: 100%|████████████████████████| Time: 0:00:00 ( 2.87 ms/it) accuracy/val: 0.968 loss/val: 0.102      Train Epoch 2: 2%|▍ | ETA: 0:00:06 (14.46 ms/it) accuracy/train: 0.977 loss/train: 0.13     Train Epoch 2: 4%|▊ | ETA: 0:00:05 (14.36 ms/it) accuracy/train: 0.984 loss/train: 0.0448     Train Epoch 2: 5%|█▏ | ETA: 0:00:05 (14.37 ms/it) accuracy/train: 0.961 loss/train: 0.243     Train Epoch 2: 7%|█▋ | ETA: 0:00:05 (14.34 ms/it) accuracy/train: 0.945 loss/train: 0.13     Train Epoch 2: 9%|██ | ETA: 0:00:05 (14.33 ms/it) accuracy/train: 0.945 loss/train: 0.169     Train Epoch 2: 11%|██▍ | ETA: 0:00:05 (14.31 ms/it) accuracy/train: 0.961 loss/train: 0.0688     Train Epoch 2: 12%|██▊ | ETA: 0:00:05 (14.82 ms/it) accuracy/train: 0.945 loss/train: 0.105     Train Epoch 2: 14%|███▏ | ETA: 0:00:05 (14.75 ms/it) accuracy/train: 0.938 loss/train: 0.189     Train Epoch 2: 16%|███▌ | ETA: 0:00:05 (14.69 ms/it) accuracy/train: 0.945 loss/train: 0.152     Train Epoch 2: 18%|████ | ETA: 0:00:05 (14.64 ms/it) accuracy/train: 0.977 loss/train: 0.0948     Train Epoch 2: 20%|████▍ | ETA: 0:00:04 (14.62 ms/it) accuracy/train: 0.977 loss/train: 0.0872     Train Epoch 2: 22%|████▊ | ETA: 0:00:04 (14.59 ms/it) accuracy/train: 0.969 loss/train: 0.128     Train Epoch 2: 23%|█████▏ | ETA: 0:00:04 (14.56 ms/it) accuracy/train: 0.969 loss/train: 0.111     Train Epoch 2: 25%|█████▋ | ETA: 0:00:04 (14.54 ms/it) accuracy/train: 0.938 loss/train: 0.298     Train Epoch 2: 27%|██████ | ETA: 0:00:04 (14.64 ms/it) accuracy/train: 0.961 loss/train: 0.194     Train Epoch 2: 29%|██████▎ | ETA: 0:00:04 (14.66 ms/it) accuracy/train: 0.977 loss/train: 0.0581     Train Epoch 2: 30%|██████▋ | ETA: 0:00:04 (14.64 ms/it) accuracy/train: 0.969 loss/train: 0.14     Train Epoch 2: 32%|███████▏ | ETA: 0:00:04 (14.62 ms/it) accuracy/train: 0.961 loss/train: 0.23     Train Epoch 2: 34%|███████▌ | ETA: 0:00:04 (14.60 ms/it) accuracy/train: 0.953 loss/train: 0.161     Train Epoch 2: 36%|███████▉ | ETA: 0:00:03 (14.58 ms/it) accuracy/train: 0.938 loss/train: 0.197     Train Epoch 2: 38%|████████▍ | ETA: 0:00:03 (14.56 ms/it) accuracy/train: 0.992 loss/train: 0.0364     Train Epoch 2: 40%|████████▊ | ETA: 0:00:03 (14.55 ms/it) accuracy/train: 0.992 loss/train: 0.0277     Train Epoch 2: 41%|█████████▏ | ETA: 0:00:03 (14.55 ms/it) accuracy/train: 0.953 loss/train: 0.146     Train Epoch 2: 43%|█████████▌ | ETA: 0:00:03 (14.54 ms/it) accuracy/train: 0.961 loss/train: 0.112     Train Epoch 2: 45%|█████████▉ | ETA: 0:00:03 (14.54 ms/it) accuracy/train: 0.969 loss/train: 0.071     Train Epoch 2: 47%|██████████▎ | ETA: 0:00:03 (14.52 ms/it) accuracy/train: 0.938 loss/train: 0.261     Train Epoch 2: 49%|██████████▋ | ETA: 0:00:03 (14.51 ms/it) accuracy/train: 0.953 loss/train: 0.126     Train Epoch 2: 50%|███████████▏ | ETA: 0:00:03 (14.50 ms/it) accuracy/train: 0.961 loss/train: 0.16     Train Epoch 2: 52%|███████████▌ | ETA: 0:00:02 (14.49 ms/it) accuracy/train: 0.969 loss/train: 0.0838     Train Epoch 2: 54%|███████████▉ | ETA: 0:00:02 (14.50 ms/it) accuracy/train: 0.977 loss/train: 0.0713     Train Epoch 2: 56%|████████████▎ | ETA: 0:00:02 (14.49 ms/it) accuracy/train: 0.984 loss/train: 0.0737     Train Epoch 2: 58%|████████████▊ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.961 loss/train: 0.212     Train Epoch 2: 60%|█████████████▏ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.977 loss/train: 0.0479     Train Epoch 2: 61%|█████████████▌ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.961 loss/train: 0.0939     Train Epoch 2: 63%|█████████████▉ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.977 loss/train: 0.0567     Train Epoch 2: 65%|██████████████▎ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.945 loss/train: 0.111     Train Epoch 2: 66%|██████████████▋ | ETA: 0:00:02 (14.48 ms/it) accuracy/train: 0.977 loss/train: 0.0643     Train Epoch 2: 68%|███████████████ | ETA: 0:00:01 (14.50 ms/it) accuracy/train: 0.977 loss/train: 0.0559     Train Epoch 2: 70%|███████████████▍ | ETA: 0:00:01 (14.51 ms/it) accuracy/train: 0.961 loss/train: 0.184     Train Epoch 2: 71%|███████████████▊ | ETA: 0:00:01 (14.53 ms/it) accuracy/train: 0.969 loss/train: 0.0871     Train Epoch 2: 73%|████████████████ | ETA: 0:00:01 (14.55 ms/it) accuracy/train: 0.953 loss/train: 0.114     Train Epoch 2: 75%|████████████████▍ | ETA: 0:00:01 (14.58 ms/it) accuracy/train: 0.969 loss/train: 0.11     Train Epoch 2: 76%|████████████████▊ | ETA: 0:00:01 (14.63 ms/it) accuracy/train: 0.969 loss/train: 0.0646     Train Epoch 2: 78%|█████████████████▏ | ETA: 0:00:01 (14.67 ms/it) accuracy/train: 0.984 loss/train: 0.0483     Train Epoch 2: 79%|█████████████████▌ | ETA: 0:00:01 (14.71 ms/it) accuracy/train: 0.984 loss/train: 0.0494     Train Epoch 2: 81%|█████████████████▊ | ETA: 0:00:01 (14.76 ms/it) accuracy/train: 0.969 loss/train: 0.0974     Train Epoch 2: 82%|██████████████████▏ | ETA: 0:00:01 (14.81 ms/it) accuracy/train: 0.984 loss/train: 0.0411     Train Epoch 2: 84%|██████████████████▍ | ETA: 0:00:01 (14.85 ms/it) accuracy/train: 0.938 loss/train: 0.167     Train Epoch 2: 85%|██████████████████▊ | ETA: 0:00:00 (14.90 ms/it) accuracy/train: 0.969 loss/train: 0.0994     Train Epoch 2: 86%|███████████████████ | ETA: 0:00:00 (14.94 ms/it) accuracy/train: 0.961 loss/train: 0.152     Train Epoch 2: 88%|███████████████████▍ | ETA: 0:00:00 (14.98 ms/it) accuracy/train: 0.977 loss/train: 0.0624     Train Epoch 2: 89%|███████████████████▋ | ETA: 0:00:00 (15.03 ms/it) accuracy/train: 0.977 loss/train: 0.0728     Train Epoch 2: 91%|████████████████████ | ETA: 0:00:00 (15.07 ms/it) accuracy/train: 0.945 loss/train: 0.14     Train Epoch 2: 92%|████████████████████▎ | ETA: 0:00:00 (15.10 ms/it) accuracy/train: 0.969 loss/train: 0.0875     Train Epoch 2: 94%|████████████████████▋ | ETA: 0:00:00 (15.14 ms/it) accuracy/train: 0.969 loss/train: 0.102     Train Epoch 2: 95%|████████████████████▉ | ETA: 0:00:00 (15.18 ms/it) accuracy/train: 1.0 loss/train: 0.0196     Train Epoch 2: 96%|█████████████████████▎| ETA: 0:00:00 (15.22 ms/it) accuracy/train: 0.961 loss/train: 0.111     Train Epoch 2: 98%|█████████████████████▌| ETA: 0:00:00 (15.26 ms/it) accuracy/train: 0.898 loss/train: 0.393     Train Epoch 2: 99%|█████████████████████▉| ETA: 0:00:00 (15.29 ms/it) accuracy/train: 0.977 loss/train: 0.114     Train Epoch 2: 100%|██████████████████████| Time: 0:00:06 (15.31 ms/it) accuracy/train: 0.982 loss/train: 0.0517   Val Epoch 2: 74%|█████████████████▉ | ETA: 0:00:00 ( 2.88 ms/it) accuracy/val: 0.967 loss/val: 0.116        Val Epoch 2: 100%|████████████████████████| Time: 0:00:00 ( 2.87 ms/it) accuracy/val: 0.967 loss/val: 0.117      Train Epoch 3: 1%|▍ | ETA: 0:00:07 (18.04 ms/it) accuracy/train: 0.969 loss/train: 0.108     Train Epoch 3: 3%|▋ | ETA: 0:00:07 (17.90 ms/it) accuracy/train: 0.969 loss/train: 0.15     Train Epoch 3: 4%|█ | ETA: 0:00:07 (17.88 ms/it) accuracy/train: 0.969 loss/train: 0.0928     Train Epoch 3: 6%|█▎ | ETA: 0:00:07 (17.82 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 7%|█▋ | ETA: 0:00:06 (17.80 ms/it) accuracy/train: 0.969 loss/train: 0.0809     Train Epoch 3: 9%|█▉ | ETA: 0:00:06 (17.84 ms/it) accuracy/train: 0.969 loss/train: 0.0554     Train Epoch 3: 10%|██▎ | ETA: 0:00:06 (17.84 ms/it) accuracy/train: 0.992 loss/train: 0.0462     Train Epoch 3: 11%|██▌ | ETA: 0:00:06 (17.83 ms/it) accuracy/train: 0.992 loss/train: 0.0211     Train Epoch 3: 13%|██▉ | ETA: 0:00:06 (17.81 ms/it) accuracy/train: 0.992 loss/train: 0.0276     Train Epoch 3: 14%|███▏ | ETA: 0:00:06 (17.80 ms/it) accuracy/train: 0.977 loss/train: 0.0673     Train Epoch 3: 16%|███▌ | ETA: 0:00:06 (17.92 ms/it) accuracy/train: 0.984 loss/train: 0.0626     Train Epoch 3: 17%|███▊ | ETA: 0:00:06 (17.93 ms/it) accuracy/train: 0.984 loss/train: 0.0355     Train Epoch 3: 18%|████▏ | ETA: 0:00:06 (17.91 ms/it) accuracy/train: 0.992 loss/train: 0.0174     Train Epoch 3: 20%|████▍ | ETA: 0:00:06 (17.90 ms/it) accuracy/train: 0.992 loss/train: 0.0167     Train Epoch 3: 21%|████▊ | ETA: 0:00:05 (17.89 ms/it) accuracy/train: 0.969 loss/train: 0.125     Train Epoch 3: 23%|█████ | ETA: 0:00:05 (17.92 ms/it) accuracy/train: 0.984 loss/train: 0.0402     Train Epoch 3: 24%|█████▍ | ETA: 0:00:05 (17.92 ms/it) accuracy/train: 0.977 loss/train: 0.0822     Train Epoch 3: 26%|█████▋ | ETA: 0:00:05 (17.91 ms/it) accuracy/train: 0.984 loss/train: 0.0276     Train Epoch 3: 27%|██████ | ETA: 0:00:05 (17.91 ms/it) accuracy/train: 0.984 loss/train: 0.0739     Train Epoch 3: 28%|██████▎ | ETA: 0:00:05 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.0373     Train Epoch 3: 30%|██████▋ | ETA: 0:00:05 (17.90 ms/it) accuracy/train: 0.977 loss/train: 0.0638     Train Epoch 3: 31%|██████▉ | ETA: 0:00:05 (17.90 ms/it) accuracy/train: 0.992 loss/train: 0.0354     Train Epoch 3: 33%|███████▎ | ETA: 0:00:05 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.0631     Train Epoch 3: 34%|███████▌ | ETA: 0:00:04 (17.89 ms/it) accuracy/train: 0.992 loss/train: 0.0297     Train Epoch 3: 36%|███████▉ | ETA: 0:00:04 (17.89 ms/it) accuracy/train: 0.984 loss/train: 0.0525     Train Epoch 3: 37%|████████▏ | ETA: 0:00:04 (17.91 ms/it) accuracy/train: 0.992 loss/train: 0.0285     Train Epoch 3: 38%|████████▌ | ETA: 0:00:04 (17.91 ms/it) accuracy/train: 0.984 loss/train: 0.0465     Train Epoch 3: 40%|████████▊ | ETA: 0:00:04 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.0409     Train Epoch 3: 41%|█████████▏ | ETA: 0:00:04 (17.90 ms/it) accuracy/train: 1.0 loss/train: 0.00411     Train Epoch 3: 43%|█████████▍ | ETA: 0:00:04 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.056     Train Epoch 3: 44%|█████████▊ | ETA: 0:00:04 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.0594     Train Epoch 3: 45%|██████████ | ETA: 0:00:04 (17.90 ms/it) accuracy/train: 0.984 loss/train: 0.0346     Train Epoch 3: 47%|██████████▍ | ETA: 0:00:04 (17.91 ms/it) accuracy/train: 0.969 loss/train: 0.0761     Train Epoch 3: 48%|██████████▋ | ETA: 0:00:03 (17.90 ms/it) accuracy/train: 0.961 loss/train: 0.114     Train Epoch 3: 50%|███████████ | ETA: 0:00:03 (17.91 ms/it) accuracy/train: 1.0 loss/train: 0.0161     Train Epoch 3: 51%|███████████▎ | ETA: 0:00:03 (17.92 ms/it) accuracy/train: 0.984 loss/train: 0.0319     Train Epoch 3: 53%|███████████▋ | ETA: 0:00:03 (17.92 ms/it) accuracy/train: 0.992 loss/train: 0.0451     Train Epoch 3: 54%|███████████▉ | ETA: 0:00:03 (17.92 ms/it) accuracy/train: 1.0 loss/train: 0.0212     Train Epoch 3: 55%|████████████▎ | ETA: 0:00:03 (17.91 ms/it) accuracy/train: 0.984 loss/train: 0.0664     Train Epoch 3: 57%|████████████▌ | ETA: 0:00:03 (17.91 ms/it) accuracy/train: 0.992 loss/train: 0.0303     Train Epoch 3: 58%|████████████▉ | ETA: 0:00:03 (17.91 ms/it) accuracy/train: 0.977 loss/train: 0.0592     Train Epoch 3: 60%|█████████████▏ | ETA: 0:00:03 (17.91 ms/it) accuracy/train: 0.961 loss/train: 0.0761     Train Epoch 3: 61%|█████████████▌ | ETA: 0:00:02 (17.91 ms/it) accuracy/train: 1.0 loss/train: 0.0134     Train Epoch 3: 63%|█████████████▊ | ETA: 0:00:02 (17.91 ms/it) accuracy/train: 0.984 loss/train: 0.0547     Train Epoch 3: 64%|██████████████▏ | ETA: 0:00:02 (17.92 ms/it) accuracy/train: 0.961 loss/train: 0.189     Train Epoch 3: 65%|██████████████▍ | ETA: 0:00:02 (17.92 ms/it) accuracy/train: 0.984 loss/train: 0.0436     Train Epoch 3: 67%|██████████████▊ | ETA: 0:00:02 (17.93 ms/it) accuracy/train: 0.984 loss/train: 0.0597     Train Epoch 3: 68%|███████████████ | ETA: 0:00:02 (17.93 ms/it) accuracy/train: 0.992 loss/train: 0.03     Train Epoch 3: 70%|███████████████▍ | ETA: 0:00:02 (17.93 ms/it) accuracy/train: 1.0 loss/train: 0.0127     Train Epoch 3: 71%|███████████████▋ | ETA: 0:00:02 (17.93 ms/it) accuracy/train: 0.969 loss/train: 0.0528     Train Epoch 3: 73%|████████████████ | ETA: 0:00:02 (17.93 ms/it) accuracy/train: 0.992 loss/train: 0.0344     Train Epoch 3: 74%|████████████████▎ | ETA: 0:00:01 (17.93 ms/it) accuracy/train: 1.0 loss/train: 0.0133     Train Epoch 3: 75%|████████████████▋ | ETA: 0:00:01 (17.93 ms/it) accuracy/train: 0.992 loss/train: 0.0448     Train Epoch 3: 77%|████████████████▉ | ETA: 0:00:01 (17.93 ms/it) accuracy/train: 0.984 loss/train: 0.0417     Train Epoch 3: 78%|█████████████████▎ | ETA: 0:00:01 (17.94 ms/it) accuracy/train: 0.984 loss/train: 0.0468     Train Epoch 3: 80%|█████████████████▌ | ETA: 0:00:01 (17.94 ms/it) accuracy/train: 0.984 loss/train: 0.0528     Train Epoch 3: 81%|█████████████████▉ | ETA: 0:00:01 (17.94 ms/it) accuracy/train: 0.977 loss/train: 0.0473     Train Epoch 3: 82%|██████████████████▏ | ETA: 0:00:01 (17.94 ms/it) accuracy/train: 0.969 loss/train: 0.0957     Train Epoch 3: 84%|██████████████████▌ | ETA: 0:00:01 (17.95 ms/it) accuracy/train: 0.992 loss/train: 0.0143     Train Epoch 3: 85%|██████████████████▊ | ETA: 0:00:01 (17.95 ms/it) accuracy/train: 0.992 loss/train: 0.0281     Train Epoch 3: 87%|███████████████████▏ | ETA: 0:00:01 (17.95 ms/it) accuracy/train: 0.992 loss/train: 0.0291     Train Epoch 3: 88%|███████████████████▍ | ETA: 0:00:00 (17.95 ms/it) accuracy/train: 1.0 loss/train: 0.0103     Train Epoch 3: 90%|███████████████████▊ | ETA: 0:00:00 (17.95 ms/it) accuracy/train: 0.977 loss/train: 0.0663     Train Epoch 3: 91%|████████████████████ | ETA: 0:00:00 (17.95 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 3: 92%|████████████████████▍ | ETA: 0:00:00 (17.96 ms/it) accuracy/train: 0.984 loss/train: 0.101     Train Epoch 3: 94%|████████████████████▋ | ETA: 0:00:00 (17.96 ms/it) accuracy/train: 1.0 loss/train: 0.0173     Train Epoch 3: 95%|█████████████████████ | ETA: 0:00:00 (17.96 ms/it) accuracy/train: 1.0 loss/train: 0.0158     Train Epoch 3: 97%|█████████████████████▎| ETA: 0:00:00 (17.97 ms/it) accuracy/train: 0.984 loss/train: 0.0428     Train Epoch 3: 98%|█████████████████████▋| ETA: 0:00:00 (18.00 ms/it) accuracy/train: 1.0 loss/train: 0.00542     Train Epoch 3: 100%|█████████████████████▉| ETA: 0:00:00 (18.01 ms/it) accuracy/train: 0.984 loss/train: 0.047     Train Epoch 3: 100%|██████████████████████| Time: 0:00:07 (18.01 ms/it) accuracy/train: 0.982 loss/train: 0.0556 Val Epoch 3: 68%|████████████████▍ | ETA: 0:00:00 ( 3.13 ms/it) accuracy/val: 0.979 loss/val: 0.0731     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 3.04 ms/it) accuracy/val: 0.98 loss/val: 0.0722 [ Info: GPUs available: false, used: false [ Info: Model Summary: MLP( Chain( MLUtils.flatten, Dense(784 => 1024, relu), # 803_840 parameters Dense(1024 => 10), # 10_250 parameters ), ) # Total: 4 arrays, 814_090 parameters, 3.106 MiB. [ Info: Run Directory: /home/pkgeval/.julia/packages/Tsunami/5iz8R/examples/MLP_MNIST/tsunami_logs/run_2 Val Epoch 3: 70%|████████████████▉ | ETA: 0:00:00 ( 3.07 ms/it) accuracy/val: 0.98 loss/val: 0.0756     Val Epoch 3: 100%|████████████████████████| Time: 0:00:00 ( 3.09 ms/it) accuracy/val: 0.98 loss/val: 0.0722   Train Epoch 4: 1%|▍ | ETA: 0:00:08 (19.80 ms/it) accuracy/train: 1.0 loss/train: 0.00802     Train Epoch 4: 3%|▋ | ETA: 0:00:08 (19.73 ms/it) accuracy/train: 0.984 loss/train: 0.0375     Train Epoch 4: 4%|▉ | ETA: 0:00:09 (22.23 ms/it) accuracy/train: 0.984 loss/train: 0.0247     Train Epoch 4: 5%|█▏ | ETA: 0:00:08 (21.12 ms/it) accuracy/train: 1.0 loss/train: 0.0163     Train Epoch 4: 7%|█▌ | ETA: 0:00:08 (20.52 ms/it) accuracy/train: 1.0 loss/train: 0.00958     Train Epoch 4: 8%|█▊ | ETA: 0:00:07 (20.10 ms/it) accuracy/train: 0.992 loss/train: 0.0409     Train Epoch 4: 9%|██▏ | ETA: 0:00:07 (19.80 ms/it) accuracy/train: 1.0 loss/train: 0.0116     Train Epoch 4: 11%|██▍ | ETA: 0:00:07 (19.69 ms/it) accuracy/train: 0.992 loss/train: 0.0238     Train Epoch 4: 12%|██▊ | ETA: 0:00:07 (19.69 ms/it) accuracy/train: 0.977 loss/train: 0.0451     Train Epoch 4: 14%|███ | ETA: 0:00:07 (19.93 ms/it) accuracy/train: 0.984 loss/train: 0.0653     Train Epoch 4: 15%|███▎ | ETA: 0:00:07 (20.01 ms/it) accuracy/train: 0.992 loss/train: 0.0231     Train Epoch 4: 16%|███▌ | ETA: 0:00:07 (20.08 ms/it) accuracy/train: 1.0 loss/train: 0.014     Train Epoch 4: 17%|███▊ | ETA: 0:00:07 (20.27 ms/it) accuracy/train: 0.992 loss/train: 0.0111     Train Epoch 4: 18%|████▏ | ETA: 0:00:06 (20.10 ms/it) accuracy/train: 0.984 loss/train: 0.0643     Train Epoch 4: 20%|████▍ | ETA: 0:00:06 (19.97 ms/it) accuracy/train: 1.0 loss/train: 0.00372     Train Epoch 4: 21%|████▊ | ETA: 0:00:06 (19.84 ms/it) accuracy/train: 0.992 loss/train: 0.0139     Train Epoch 4: 23%|█████ | ETA: 0:00:06 (19.73 ms/it) accuracy/train: 0.992 loss/train: 0.0165     Train Epoch 4: 24%|█████▍ | ETA: 0:00:06 (19.63 ms/it) accuracy/train: 0.992 loss/train: 0.0202     Train Epoch 4: 26%|█████▋ | ETA: 0:00:06 (19.55 ms/it) accuracy/train: 0.992 loss/train: 0.0492     Train Epoch 4: 27%|██████ | ETA: 0:00:06 (19.54 ms/it) accuracy/train: 0.992 loss/train: 0.0208     Train Epoch 4: 28%|██████▎ | ETA: 0:00:05 (19.51 ms/it) accuracy/train: 0.992 loss/train: 0.0297     Train Epoch 4: 30%|██████▋ | ETA: 0:00:05 (19.44 ms/it) accuracy/train: 0.992 loss/train: 0.045     Train Epoch 4: 31%|██████▉ | ETA: 0:00:05 (19.43 ms/it) accuracy/train: 0.992 loss/train: 0.0211     Train Epoch 4: 33%|███████▎ | ETA: 0:00:05 (19.38 ms/it) accuracy/train: 0.984 loss/train: 0.0564     Train Epoch 4: 34%|███████▌ | ETA: 0:00:05 (19.32 ms/it) accuracy/train: 1.0 loss/train: 0.0218     Train Epoch 4: 36%|███████▉ | ETA: 0:00:05 (19.27 ms/it) accuracy/train: 0.992 loss/train: 0.0303     Train Epoch 4: 37%|████████▏ | ETA: 0:00:05 (19.23 ms/it) accuracy/train: 1.0 loss/train: 0.0103     Train Epoch 4: 38%|████████▌ | ETA: 0:00:04 (19.18 ms/it) accuracy/train: 1.0 loss/train: 0.00343     Train Epoch 4: 40%|████████▊ | ETA: 0:00:04 (19.14 ms/it) accuracy/train: 0.992 loss/train: 0.0382     Train Epoch 4: 41%|█████████▏ | ETA: 0:00:04 (19.11 ms/it) accuracy/train: 1.0 loss/train: 0.00831     Train Epoch 4: 43%|█████████▍ | ETA: 0:00:04 (19.07 ms/it) accuracy/train: 0.984 loss/train: 0.0222     Train Epoch 4: 44%|█████████▊ | ETA: 0:00:04 (19.05 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 45%|██████████ | ETA: 0:00:04 (19.04 ms/it) accuracy/train: 0.992 loss/train: 0.0408     Train Epoch 4: 47%|██████████▍ | ETA: 0:00:04 (19.01 ms/it) accuracy/train: 0.977 loss/train: 0.111     Train Epoch 4: 48%|██████████▋ | ETA: 0:00:04 (18.98 ms/it) accuracy/train: 1.0 loss/train: 0.00278     Train Epoch 4: 50%|███████████ | ETA: 0:00:04 (18.96 ms/it) accuracy/train: 0.984 loss/train: 0.0634     Train Epoch 4: 51%|███████████▎ | ETA: 0:00:03 (18.94 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 53%|███████████▋ | ETA: 0:00:03 (18.92 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 4: 54%|███████████▉ | ETA: 0:00:03 (18.89 ms/it) accuracy/train: 0.992 loss/train: 0.0142     Train Epoch 4: 55%|████████████▎ | ETA: 0:00:03 (18.87 ms/it) accuracy/train: 1.0 loss/train: 0.00493     Train Epoch 4: 57%|████████████▌ | ETA: 0:00:03 (18.85 ms/it) accuracy/train: 1.0 loss/train: 0.00727     Train Epoch 4: 58%|████████████▉ | ETA: 0:00:03 (18.84 ms/it) accuracy/train: 1.0 loss/train: 0.0119     Train Epoch 4: 60%|█████████████▏ | ETA: 0:00:03 (18.84 ms/it) accuracy/train: 1.0 loss/train: 0.00523     Train Epoch 4: 61%|█████████████▌ | ETA: 0:00:03 (18.82 ms/it) accuracy/train: 1.0 loss/train: 0.0122     Train Epoch 4: 63%|█████████████▊ | ETA: 0:00:02 (18.81 ms/it) accuracy/train: 0.984 loss/train: 0.0277     Train Epoch 4: 64%|██████████████▏ | ETA: 0:00:02 (18.79 ms/it) accuracy/train: 1.0 loss/train: 0.0155     Train Epoch 4: 65%|██████████████▍ | ETA: 0:00:02 (18.78 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 4: 67%|██████████████▊ | ETA: 0:00:02 (18.77 ms/it) accuracy/train: 0.977 loss/train: 0.0933     Train Epoch 4: 68%|███████████████ | ETA: 0:00:02 (18.75 ms/it) accuracy/train: 0.984 loss/train: 0.0607     Train Epoch 4: 70%|███████████████▍ | ETA: 0:00:02 (18.75 ms/it) accuracy/train: 0.992 loss/train: 0.0439     Train Epoch 4: 71%|███████████████▋ | ETA: 0:00:02 (18.73 ms/it) accuracy/train: 1.0 loss/train: 0.00757     Train Epoch 4: 73%|████████████████ | ETA: 0:00:02 (18.72 ms/it) accuracy/train: 1.0 loss/train: 0.00584     Train Epoch 4: 74%|████████████████▎ | ETA: 0:00:02 (18.72 ms/it) accuracy/train: 0.977 loss/train: 0.0836     Train Epoch 4: 75%|████████████████▋ | ETA: 0:00:01 (18.71 ms/it) accuracy/train: 0.984 loss/train: 0.0208     Train Epoch 4: 77%|████████████████▉ | ETA: 0:00:01 (18.70 ms/it) accuracy/train: 0.984 loss/train: 0.064     Train Epoch 4: 78%|█████████████████▎ | ETA: 0:00:01 (18.69 ms/it) accuracy/train: 0.992 loss/train: 0.0237     Train Epoch 4: 80%|█████████████████▌ | ETA: 0:00:01 (18.68 ms/it) accuracy/train: 1.0 loss/train: 0.00773     Train Epoch 4: 81%|█████████████████▉ | ETA: 0:00:01 (18.67 ms/it) accuracy/train: 0.992 loss/train: 0.028     Train Epoch 4: 82%|██████████████████▏ | ETA: 0:00:01 (18.66 ms/it) accuracy/train: 0.977 loss/train: 0.0399     Train Epoch 4: 84%|██████████████████▌ | ETA: 0:00:01 (18.65 ms/it) accuracy/train: 0.984 loss/train: 0.056     Train Epoch 4: 85%|██████████████████▊ | ETA: 0:00:01 (18.65 ms/it) accuracy/train: 1.0 loss/train: 0.0136     Train Epoch 4: 87%|███████████████████▏ | ETA: 0:00:01 (18.64 ms/it) accuracy/train: 0.977 loss/train: 0.0503     Train Epoch 4: 88%|███████████████████▍ | ETA: 0:00:00 (18.64 ms/it) accuracy/train: 1.0 loss/train: 0.0085     Train Epoch 4: 90%|███████████████████▊ | ETA: 0:00:00 (18.63 ms/it) accuracy/train: 1.0 loss/train: 0.0108     Train Epoch 4: 91%|████████████████████ | ETA: 0:00:00 (18.63 ms/it) accuracy/train: 1.0 loss/train: 0.0114     Train Epoch 4: 92%|████████████████████▍ | ETA: 0:00:00 (18.62 ms/it) accuracy/train: 1.0 loss/train: 0.0102     Train Epoch 4: 94%|████████████████████▋ | ETA: 0:00:00 (18.63 ms/it) accuracy/train: 0.984 loss/train: 0.0286     Train Epoch 4: 95%|█████████████████████ | ETA: 0:00:00 (18.62 ms/it) accuracy/train: 0.984 loss/train: 0.0443     Train Epoch 4: 97%|█████████████████████▎| ETA: 0:00:00 (18.62 ms/it) accuracy/train: 1.0 loss/train: 0.00735     Train Epoch 4: 98%|█████████████████████▋| ETA: 0:00:00 (18.61 ms/it) accuracy/train: 0.984 loss/train: 0.0647     Train Epoch 4: 100%|█████████████████████▉| ETA: 0:00:00 (18.61 ms/it) accuracy/train: 0.992 loss/train: 0.011     Train Epoch 4: 100%|██████████████████████| Time: 0:00:07 (18.61 ms/it) accuracy/train: 0.991 loss/train: 0.0187   Val Epoch 4: 74%|█████████████████▉ | ETA: 0:00:00 ( 2.89 ms/it) accuracy/val: 0.978 loss/val: 0.0802        Val Epoch 4: 100%|████████████████████████| Time: 0:00:00 ( 3.13 ms/it) accuracy/val: 0.98 loss/val: 0.0706      Train Epoch 5: 1%|▍ | ETA: 0:00:07 (18.28 ms/it) accuracy/train: 1.0 loss/train: 0.00192     Train Epoch 5: 3%|▋ | ETA: 0:00:07 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00808     Train Epoch 5: 4%|█ | ETA: 0:00:07 (18.20 ms/it) accuracy/train: 0.984 loss/train: 0.0293     Train Epoch 5: 6%|█▎ | ETA: 0:00:07 (18.21 ms/it) accuracy/train: 1.0 loss/train: 0.011     Train Epoch 5: 7%|█▋ | ETA: 0:00:07 (18.20 ms/it) accuracy/train: 1.0 loss/train: 0.00932     Train Epoch 5: 9%|█▉ | ETA: 0:00:07 (18.20 ms/it) accuracy/train: 1.0 loss/train: 0.00404     Train Epoch 5: 10%|██▎ | ETA: 0:00:06 (18.18 ms/it) accuracy/train: 0.992 loss/train: 0.0352     Train Epoch 5: 11%|██▌ | ETA: 0:00:06 (18.18 ms/it) accuracy/train: 0.992 loss/train: 0.015     Train Epoch 5: 13%|██▉ | ETA: 0:00:06 (18.18 ms/it) accuracy/train: 0.992 loss/train: 0.0201     Train Epoch 5: 14%|███▏ | ETA: 0:00:06 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.0118     Train Epoch 5: 16%|███▌ | ETA: 0:00:06 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.0125     Train Epoch 5: 17%|███▊ | ETA: 0:00:06 (18.21 ms/it) accuracy/train: 0.992 loss/train: 0.0314     Train Epoch 5: 18%|████▏ | ETA: 0:00:06 (18.20 ms/it) accuracy/train: 0.992 loss/train: 0.0485     Train Epoch 5: 20%|████▍ | ETA: 0:00:06 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00355     Train Epoch 5: 21%|████▊ | ETA: 0:00:06 (18.25 ms/it) accuracy/train: 1.0 loss/train: 0.00131     Train Epoch 5: 23%|█████ | ETA: 0:00:05 (18.24 ms/it) accuracy/train: 1.0 loss/train: 0.00798     Train Epoch 5: 24%|█████▍ | ETA: 0:00:05 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.0102     Train Epoch 5: 26%|█████▋ | ETA: 0:00:05 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.00404     Train Epoch 5: 27%|██████ | ETA: 0:00:05 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.0069     Train Epoch 5: 28%|██████▎ | ETA: 0:00:05 (18.25 ms/it) accuracy/train: 1.0 loss/train: 0.00932     Train Epoch 5: 30%|██████▋ | ETA: 0:00:05 (18.25 ms/it) accuracy/train: 1.0 loss/train: 0.00236     Train Epoch 5: 31%|██████▉ | ETA: 0:00:05 (18.24 ms/it) accuracy/train: 0.992 loss/train: 0.0185     Train Epoch 5: 33%|███████▎ | ETA: 0:00:05 (18.24 ms/it) accuracy/train: 1.0 loss/train: 0.00989     Train Epoch 5: 34%|███████▌ | ETA: 0:00:05 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00942     Train Epoch 5: 36%|███████▉ | ETA: 0:00:04 (18.23 ms/it) accuracy/train: 0.992 loss/train: 0.0202     Train Epoch 5: 37%|████████▏ | ETA: 0:00:04 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.0077     Train Epoch 5: 38%|████████▌ | ETA: 0:00:04 (18.22 ms/it) accuracy/train: 0.984 loss/train: 0.0331     Train Epoch 5: 40%|████████▊ | ETA: 0:00:04 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.00532     Train Epoch 5: 41%|█████████▏ | ETA: 0:00:04 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.00431     Train Epoch 5: 43%|█████████▍ | ETA: 0:00:04 (18.24 ms/it) accuracy/train: 0.992 loss/train: 0.0456     Train Epoch 5: 44%|█████████▊ | ETA: 0:00:04 (18.24 ms/it) accuracy/train: 0.992 loss/train: 0.0164     Train Epoch 5: 45%|██████████ | ETA: 0:00:04 (18.24 ms/it) accuracy/train: 1.0 loss/train: 0.0139     Train Epoch 5: 47%|██████████▍ | ETA: 0:00:04 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00638     Train Epoch 5: 48%|██████████▋ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 0.984 loss/train: 0.0355     Train Epoch 5: 50%|███████████ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 0.992 loss/train: 0.0603     Train Epoch 5: 51%|███████████▎ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 0.984 loss/train: 0.0277     Train Epoch 5: 53%|███████████▋ | ETA: 0:00:03 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.00959     Train Epoch 5: 54%|███████████▉ | ETA: 0:00:03 (18.22 ms/it) accuracy/train: 1.0 loss/train: 0.0105     Train Epoch 5: 55%|████████████▎ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00707     Train Epoch 5: 57%|████████████▌ | ETA: 0:00:03 (18.24 ms/it) accuracy/train: 1.0 loss/train: 0.0126     Train Epoch 5: 58%|████████████▉ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00706     Train Epoch 5: 60%|█████████████▏ | ETA: 0:00:03 (18.23 ms/it) accuracy/train: 0.984 loss/train: 0.0455     Train Epoch 5: 61%|█████████████▌ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 0.992 loss/train: 0.0326     Train Epoch 5: 63%|█████████████▊ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00502     Train Epoch 5: 64%|██████████████▏ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.0113     Train Epoch 5: 65%|██████████████▍ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 0.984 loss/train: 0.0358     Train Epoch 5: 67%|██████████████▊ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.0191     Train Epoch 5: 68%|███████████████ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 0.992 loss/train: 0.0237     Train Epoch 5: 70%|███████████████▍ | ETA: 0:00:02 (18.23 ms/it) accuracy/train: 1.0 loss/train: 0.00994     Train Epoch 5: 71%|███████████████▋ | ETA: 0:00:02 (18.26 ms/it) accuracy/train: 1.0 loss/train: 0.0149     Train Epoch 5: 73%|████████████████ | ETA: 0:00:02 (18.26 ms/it) accuracy/train: 0.984 loss/train: 0.0209     Train Epoch 5: 74%|████████████████▎ | ETA: 0:00:02 (18.26 ms/it) accuracy/train: 0.992 loss/train: 0.0287     Train Epoch 5: 75%|████████████████▋ | ETA: 0:00:01 (18.26 ms/it) accuracy/train: 1.0 loss/train: 0.0115     Train Epoch 5: 77%|████████████████▉ | ETA: 0:00:01 (18.26 ms/it) accuracy/train: 0.977 loss/train: 0.0835     Train Epoch 5: 78%|█████████████████▎ | ETA: 0:00:01 (18.26 ms/it) accuracy/train: 0.992 loss/train: 0.0259     Train Epoch 5: 80%|█████████████████▌ | ETA: 0:00:01 (18.26 ms/it) accuracy/train: 1.0 loss/train: 0.0156     Train Epoch 5: 81%|█████████████████▉ | ETA: 0:00:01 (18.26 ms/it) accuracy/train: 0.992 loss/train: 0.0386     Train Epoch 5: 82%|██████████████████▏ | ETA: 0:00:01 (18.25 ms/it) accuracy/train: 1.0 loss/train: 0.00993     Train Epoch 5: 84%|██████████████████▌ | ETA: 0:00:01 (18.25 ms/it) accuracy/train: 1.0 loss/train: 0.0109     Train Epoch 5: 85%|██████████████████▊ | ETA: 0:00:01 (18.30 ms/it) accuracy/train: 1.0 loss/train: 0.0104     Train Epoch 5: 87%|███████████████████▏ | ETA: 0:00:01 (18.30 ms/it) accuracy/train: 0.992 loss/train: 0.0246     Train Epoch 5: 88%|███████████████████▍ | ETA: 0:00:00 (18.30 ms/it) accuracy/train: 1.0 loss/train: 0.0115     Train Epoch 5: 90%|███████████████████▊ | ETA: 0:00:00 (18.30 ms/it) accuracy/train: 1.0 loss/train: 0.0152     Train Epoch 5: 91%|████████████████████ | ETA: 0:00:00 (18.30 ms/it) accuracy/train: 1.0 loss/train: 0.00959     Train Epoch 5: 92%|████████████████████▍ | ETA: 0:00:00 (18.29 ms/it) accuracy/train: 0.984 loss/train: 0.0577     Train Epoch 5: 94%|████████████████████▋ | ETA: 0:00:00 (18.29 ms/it) accuracy/train: 1.0 loss/train: 0.00717     Train Epoch 5: 95%|█████████████████████ | ETA: 0:00:00 (18.29 ms/it) accuracy/train: 0.992 loss/train: 0.0174     Train Epoch 5: 97%|█████████████████████▎| ETA: 0:00:00 (18.29 ms/it) accuracy/train: 0.992 loss/train: 0.0392     Train Epoch 5: 98%|█████████████████████▋| ETA: 0:00:00 (18.28 ms/it) accuracy/train: 0.992 loss/train: 0.0164     Train Epoch 5: 100%|█████████████████████▉| ETA: 0:00:00 (18.29 ms/it) accuracy/train: 1.0 loss/train: 0.0104     Train Epoch 5: 100%|██████████████████████| Time: 0:00:07 (18.29 ms/it) accuracy/train: 0.991 loss/train: 0.0188 Val Epoch 5: 74%|█████████████████▉ | ETA: 0:00:00 ( 2.88 ms/it) accuracy/val: 0.982 loss/val: 0.073     Val Epoch 5: 100%|████████████████████████| Time: 0:00:00 ( 2.88 ms/it) accuracy/val: 0.982 loss/val: 0.0687 Testing: 24%|██████▊ | ETA: 0:00:00 ( 5.38 ms/it) accuracy/test: 0.974 loss/test: 0.0879     Testing: 67%|██████████████████▊ | ETA: 0:00:00 ( 3.83 ms/it) accuracy/test: 0.978 loss/test: 0.0821     Testing: 100%|████████████████████████████| Time: 0:00:00 ( 3.52 ms/it) accuracy/test: 0.982 loss/test: 0.0671 Test Summary: | Time Examples | None 1m19.0s Testing Tsunami tests passed Testing completed after 444.9s PkgEval succeeded after 1129.88s